This week, scientists at IBM research unveiled a brain-inspired computer and ecosystem. From their press release on the so-called SyNAPSE chip:

Scientists from IBM unveiled the first neurosynaptic computer chip to achieve an unprecedented scale of one million programmable neurons, 256 million programmable synapses and 46 billion synaptic operations per second per watt. At 5.4 billion transistors, this fully functional and production-scale chip is currently one of the largest CMOS chips ever built, yet, while running at biological real time, it consumes a minuscule 70mW—orders of magnitude less power than a modern microprocessor.

MIT Technology Review has a good summary as well:

IBM’s SyNapse chip processes information using a network of just over one million “neurons,” which communicate with one another using electrical spikes—as actual neurons do. The chip uses the same basic components as today’s commercial chips—silicon transistors. But its transistors are configured to mimic the behavior of both neurons and the connections—synapses—between them.

The SyNapse chip breaks with a design known as the Von Neuman architecture that has underpinned computer chips for decades. Although researchers have been experimenting with chips modeled on brains—known as neuromorphic chips—since the late 1980s, until now all have been many times less complex, and not powerful enough to be practical (see “Thinking in Silicon”). Details of the chip were published today in the journal Science.

The new chip is not yet a product, but it is powerful enough to work on real-world problems. In a demonstration at IBM’s Almaden research center, MIT Technology Review saw one recognize cars, people, and bicycles in video of a road intersection. A nearby laptop that had been programed to do the same task processed the footage 100 times slower than real time, and it consumed 100,000 times as much power as the IBM chip. IBM researchers are now experimenting with connecting multiple SyNapse chips together, and they hope to build a supercomputer using thousands.

I think this kind of experimentation is fascinating. You can read more at Science Magazine (subscription required to view full text).